Future communication networks will need to have both high capacity and high connectivity, to meet the demands set by different applications (e.g., multi-media, parallel processing). High capacity is needed to convey large amounts of information to a number of users, and high connectivity is needed to support a large number of users.
There are two main advantages when using several dimensions, the first being reduced complexity and the second being increased connectivity (i.e., scalability). The reasoning behind these advantages is that it is hard to achieve a large number of distinct channels in one dimension due to limits set by physical factors (e.g., crosswalk, finite spectrum, logic speed). However, when several dimensions share the load these limits are relaxed to allow a much larger number of distinct channels; e.g., in a one-dimensional network 10,000 distinct channels crowded in that dimension may be required, whereas, when two dimensions are used only 100 distinct channels are needed in each dimension to achieve the same connectivity of 10,000. For example, 2-dimensional networks using both the wavelength and space dimensions can achieve a desired connectivity with few spatial elements and higher connectivity than one-dimensional networks (either the wavelength or space-dimension). Higher dimension networks can result in still fewer spatial elements and still higher connectivity. For example, in a three dimensional network, using time, space and wavelength, a connectivity of 1,000 can be achieved with a size of 10 for each dimension and a connectivity of 1,000,000 with a size of 100 for each dimension.
Moreover, by appropriate design, it is known to be feasible to increase connectivity in a network without a commensurate increase in hardware complexity, where hardware complexity of a network is defined as the number of basic active switching elements needed.